Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
individual
Campus
Daytona Beach
Authors' Class Standing
Julian Jesso, Senior
Lead Presenter's Name
Julian Jesso
Faculty Mentor Name
Dr. Dan Su
Abstract
Over the past two decades, extremely heavy vehicles, or superloads, have been increasingly utilized to transport heavy loads, such as prestressed concrete girders, automotive presses, transformers, wind turbine components, or other heavy loads. Since these superload have a significant effect on the infrastructure system in comparison to the regularly permitted vehicle, they should be subject to special consideration in the permitting and operation process. Despite the great research effort that has been made to improve the superload permitting process, few studies have been performed on characterization and prediction of the superload. The superload has its own distinct characteristics that differ from other vehicle loads. Thus, there is a need to better understand the characteristics of superload and to develop an analytical procedure for future use. In this research, the major focus is to develop an analytical procedure for the characterization and prediction of superload using advanced gradient boosting machine (GBM) learning algorithms.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
Yes, Spark Grant
Characterization and Prediction of Superload in Florida Using Gradient Boosting Machine Learning Algorithm
Over the past two decades, extremely heavy vehicles, or superloads, have been increasingly utilized to transport heavy loads, such as prestressed concrete girders, automotive presses, transformers, wind turbine components, or other heavy loads. Since these superload have a significant effect on the infrastructure system in comparison to the regularly permitted vehicle, they should be subject to special consideration in the permitting and operation process. Despite the great research effort that has been made to improve the superload permitting process, few studies have been performed on characterization and prediction of the superload. The superload has its own distinct characteristics that differ from other vehicle loads. Thus, there is a need to better understand the characteristics of superload and to develop an analytical procedure for future use. In this research, the major focus is to develop an analytical procedure for the characterization and prediction of superload using advanced gradient boosting machine (GBM) learning algorithms.